package neural_nets_lib

You can search for identifiers within the package.

in-package search v0.2.0

neural_nets_lib
- Ocannl
  - Operation
    
    At
    
    DO
    
    Initial_NTDSL
    
    O
    
    Initial_TDSL
    
    O
    
    NDO
    
    NDO_without_div
    
    NDO_without_pow
    
    NTDSL
    
    TDSL
  - PrintBox_utils
  - Row
    
    Variants_of_bcast
    
    Variants_of_constraint_
    
    Variants_of_dim
    
    Variants_of_dim_constraint
    
    Variants_of_kind
    
    Variants_of_row_constraint
  - Shape
  - Tensor
  - Train
    
    CDSL
    
    IDX

Legend:
Library
Module
Module type
Parameter
Class
Class type

val row_var : 
  (v:row_var -> beg_dims:dim Base.list -> bcast) Variantslib.Variant.t

val broadcastable : bcast Variantslib.Variant.t

val fold : 
  init:'acc__0 ->
  row_var:
    ('acc__0 ->
      (v:row_var -> beg_dims:dim Base.list -> bcast) Variantslib.Variant.t ->
      'acc__1) ->
  broadcastable:('acc__1 -> bcast Variantslib.Variant.t -> 'acc__2) ->
  'acc__2

val iter : 
  row_var:
    ((v:row_var -> beg_dims:dim Base.list -> bcast) Variantslib.Variant.t ->
      Base.unit) ->
  broadcastable:(bcast Variantslib.Variant.t -> Base.unit) ->
  Base.unit

val map : 
  bcast ->
  row_var:
    ((v:row_var -> beg_dims:dim Base.list -> bcast) Variantslib.Variant.t ->
      v:row_var ->
      beg_dims:dim Base.list ->
      'result__) ->
  broadcastable:(bcast Variantslib.Variant.t -> 'result__) ->
  'result__

val make_matcher : 
  row_var:
    ((v:row_var -> beg_dims:dim Base.list -> bcast) Variantslib.Variant.t ->
      'acc__0 ->
      (v:row_var -> beg_dims:dim Base.list -> 'result__) * 'acc__1) ->
  broadcastable:
    (bcast Variantslib.Variant.t ->
      'acc__1 ->
      (Base.unit -> 'result__) * 'acc__2) ->
  'acc__0 ->
  (bcast -> 'result__) * 'acc__2

val to_rank : bcast -> Base.int

val to_name : bcast -> Base.string

val descriptions : (Base.string * Base.int) Base.list